An Adaptive Prediction System for Specifying Solar Wind Conditions Near the Sun

Abstract:

Understanding Earth's space weather environment requires a clear picture of the evolving ambient solar wind flow. Critical scientific goals in space weather research and prediction are to develop, implement and optimize approaches for specifying the large-scale solar wind conditions near the Sun. Here we present an adaptive prediction system that fuses information from in situ measurements in the vicinity of Earth into solar wind models to better inform the inner boundary conditions of heliospheric models. By doing so, we attempt to advance the predictive abilities of established solar wind models such as the Wang-Sheeley-Arge approach. We validate the resulting solar wind predictions for the years 2006 to 2015. The adaptive prediction system improves all the coronal and heliospheric model combinations investigated by around 15 to 20 percent in terms of established validation metrics. We discuss why this is the case, and conclude that our findings have important implications for future developments in space weather research and prediction.